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Wireless Capsule Endoscope Localization with Phase Detection Algorithm and Adaptive Body Model.
Oleksy, Pawel; Januszkiewicz, Lukasz.
Afiliação
  • Oleksy P; Institute of Electronics, Lodz University of Technology, Politechniki 10 Street, 93-590 Lodz, Poland.
  • Januszkiewicz L; Institute of Electronics, Lodz University of Technology, Politechniki 10 Street, 93-590 Lodz, Poland.
Sensors (Basel) ; 22(6)2022 Mar 11.
Article em En | MEDLINE | ID: mdl-35336370
ABSTRACT
Wireless capsule endoscopes take and send photos of the human digestive tract, which are used for medical diagnosis. The capsule's location enables exact identification of the regions with lesions. This can be carried out by analyzing the parameters of the electromagnetic wave received from the capsule. Because the human body is a complex heterogeneous environment that impacts the propagation of wireless signals, determining the distance between the transmitter and the receiver based on the received power level is challenging. An enhanced approach of identifying the location of endoscope capsules using a wireless signal phase detection algorithm is presented in this paper. For each capsule position, this technique uses adaptive estimation of human body model permittivity. This approach was tested using computer simulations in Remcom XFdtd software using a numerical, heterogeneous human body model, as well as measurements with physical phantom. The type of transmitting antenna employed in the capsule also has a significant impact on the suggested localization method's accuracy. As a result, the helical antenna, which is smaller than the dipole, was chosen as the signal's source. For both the numerical and physical phantom studies, the proposed technique with adaptive body model enhances localization accuracy by roughly 30%.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Cápsulas Endoscópicas Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Cápsulas Endoscópicas Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article